code: def handle_min_max(preprocess, df, target):
      columns = preprocess["min-max"]
  
      result = []
  
      if isinstance(columns, str):
          columns = [columns]
  
      for column in columns:
          if column not in df.columns:
              raise Exception(
                  "The target column you provided -- {} -- does not exist".format(
                      preprocess["min-max"]
                  )
              )
          if not is_numeric_dtype(df[column]):
              raise Exception(
                  "You can only scale numeric columns, your column was a boolean."
              )
  
          scaler = MinMaxScaler()
          data = np.array(df[column]).reshape(-1, 1)
          transformed = scaler.fit(data)
          df[column] = transformed.transform(data)
  
          if column == target:
              result.append(transformed)
  
      result.append(columns)
      result.append(df)
  
      return result
  
imports: from pandas.api.types import is_numeric_dtype
  import numpy
  import pandas
  from sklearn.compose import ColumnTransformer
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.pipeline import Pipeline
  from nltk.stem import WordNetLemmatizer
  from nltk.tokenize import word_tokenize
  from nltk.corpus import stopwords
  from autocorrect import Speller
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import OrdinalEncoder
  from sklearn.impute import SimpleImputer
  from sklearn.ensemble import RandomForestRegressor
  from nylon.preprocessing.preprocessing import process_dates
  from sklearn.preprocessing import OneHotEncoder, StandardScaler, FunctionTransformer, LabelEncoder
label_desc: Use image node_id 2 for calling a global function with example usage: handle_min_max(preprocess, df, target) and returns: result